Livechart API + Data Science packages

Version 0

Pandas and Plotly

Providing a straightforward way to analyse data, these packages are widely used in machine learning.
Pandas is one of the most used libraries for data manipulation: https://pandas.pydata.org
Plotly http://plotly.com is a package for interactive data visualization, here how to get it work with jupyter lab

API Quick how-to

The service URL is nds.iaea.org/relnsd/v0/data? followed by parameters. For example:
nds.iaea.org/relnsd/v0/data?fields=decay_rads&nuclides=241am&rad_types=g.
The API v0 guide gives the detailed description, but the examples below are self explanatory

Examples

First import the libraries and set the Livechart API service

import pandas as pd
import plotly.express as px
import plotly.graph_objects as go

# the service URL
livechart = "https://nds.iaea.org/relnsd/v0/data?"

! Warning !

There have been cases in which the service returns an HTTP Error 403: Forbidden
In this case use the following workaround

import urllib.request

def lc_read_csv(url):
    req = urllib.request.Request(url)
    req.add_header('User-Agent', 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:77.0) Gecko/20100101 Firefox/77.0')
    return pd.read_csv(urllib.request.urlopen(req))

Plot γ energy vs intensity for Am-241 decay

fields=decay_rads specifies to retrieve the decay radiations, nuclides=241am specifies the parent of the decay (241am), rad_types=g for the radiation type (gamma).
On the plot below, mouseover the points to see the data. Zoom an pan are enabled, and the option to save as png is is activated when the mouse is over the top-right

# load data into a dataframe 

# this loading might not work, then use the line below
#df = pd.read_csv(livechart + "fields=decay_rads&nuclides=241am&rad_types=g")
df = lc_read_csv(livechart + "fields=decay_rads&nuclides=241am&rad_types=g")

df = df[pd.to_numeric(df['intensity'],errors='coerce').notna()] # remove blanks (unknown intensities)
df.intensity = df['intensity'].astype(float) # convert to numeric. Note how one can specify the field by attribute or by string 

fig = px.scatter(df, x="energy", y="intensity", log_y=True) # plot in log scale
fig.show()

Plot Log FT vs Transition type for Nb-100 decay

The fields to be retieved are decay_rads, the nuclide is 100-Nb, and the radiation type is bm
In this case, only the relevant fields are loaded into the dataset; then data are grouped by transition, binned, and plotted

The transition types in the plot legend are not decoded. A stands for Allowed, 1NU for 1st non unique, ...

url = livechart+"fields=decay_rads&nuclides=100nb&rad_types=bm"

# this loading might not work, then use the line below
#df=pd.read_csv(url)[['transition_type','log_ft']] # optional:load only transition_type and log_ft 
df=lc_read_csv(url)[['transition_type','log_ft']] # optional:load only transition_type and log_ft 

df = df[pd.to_numeric(df.log_ft,errors='coerce').notna()] # remove unknowns and convert to numeric values
df.log_ft = df.log_ft.astype(float)

ans = [pd.DataFrame(y) for x, y in df.groupby('transition_type', as_index=False)] # group log ft values by transition
fig = go.Figure()

for a in ans:
    ct = pd.cut(a.log_ft, bins=10)   # bin each grouping 
    tst = ct.apply(lambda x: x.mid).value_counts(sort=False)  # assign the mid value to each bin
    fig.add_trace(go.Scatter(x=tst.index.values, y = tst, mode="markers", line_shape='spline', name=a.transition_type.iloc[0]))
fig.show()

Apply further filters once the data are loaded

The pandas query function acts on the loaded dataset.
To plot the Half-life of Ac isotopes, apply the query function to the column symbol to extract the Ac isotopes

.query('symbol=="Ac"')

url=livechart+"fields=ground_states&nuclides=all"
# this loading might not work, then use the line below
#df=pd.read_csv(url)[['symbol','n','half_life_sec']].query('symbol=="Ac"') # further filter
df=lc_read_csv(url)[['symbol','n','half_life_sec']].query('symbol=="Ac"') # further filter

fig = px.scatter(df, x="n", y="half_life_sec", log_y=True)
fig.show()

3D and 2D plots of fission yields

Use the mouse to rotate and zoom

url=livechart+"fields=cumulative_fy&parents=235u"
# this loading might not work, then use the line below
#df = pd.read_csv(url) [['z_daughter','a_daughter',"element_daughter",'cumulative_fast_fy']]
df = lc_read_csv(url) [['z_daughter','a_daughter',"element_daughter",'cumulative_fast_fy']]
df = df[pd.to_numeric(df.cumulative_fast_fy,errors='coerce').notna()]
df.cumulative_fast_fy = df.cumulative_fast_fy.astype(float)
  

fig = go.Figure(data=[go.Scatter3d(
    x=df["z_daughter"],
    y=df["a_daughter"]-df["z_daughter"],
    z=df["cumulative_fast_fy"],
    mode='markers',
    text = df['a_daughter'].apply(str) + df["element_daughter"],
    marker=dict(
        size=7
        ,color=df["cumulative_fast_fy"] # array/list of values setting the color
        ,colorscale='Viridis'           # colorscale
    )
     

)])

camera = dict(                          # initial spacial settings 
    up=dict(x=0, y=0, z=1),
    center=dict(x=0, y=0, z=0),
    eye=dict(x=0.1, y=2, z=-1.5)        # view it from the bottom
)

fig.update_layout(scene_camera=camera, margin=dict(l=0, r=0, b=0, t=0), height=900)
fig.show()

# 2D plot
fig2 = go.Figure()
fig2.add_trace(go.Scatter( x=df[df.columns[1]], y=df['cumulative_fast_fy'], mode="markers", text= df['a_daughter'].apply(str) + df["element_daughter"]))
fig2.update_layout( height=1000, xaxis_title="A", yaxis_title="Yield ") 
fig2.show()
        

Column titles and samples of the available sets

• Ground state

pd.set_option('display.max_columns', None)
pd.set_option("display.max_rows", 3)

# GS
df=pd.read_csv(livechart+"fields=ground_states&nuclides=236np")
df

	z	n	symbol	radius	unc_r	energy_shift	energy	unc_e	ripl_shift	jp	half_life	operator_hl	unc_hl	unit_hl	half_life_sec	unc_hls	decay_1	decay_1_%	unc_1	decay_2	decay_2_%	unc_2	decay_3	decay_3_%	unc_3	isospin	magnetic_dipole	unc_md	electric_quadrupole	unc_eq	qbm	unc_qb	qbm_n	unc_qbmn	qa	unc_qa	qec	unc_qec	sn	unc_sn	sp	unc_sp	binding	unc_ba	atomic_mass	unc_am	mass excess	unc_me	Extraction_date
0	93	143	Np		NaN		0			(6-)	153000.0		5	Y	4.828210e+12	1.577846e+11	EC	86.3	0.8	B-	13.5	0.8	A	0.16	0.04						476.5854	503887	-6875.5708	54	5006.629	51	933.512	50	5736.2687	50	4829.6598	50	7579.2148	2136	2.360466e+08	54129	43378.094	50421	2021-08-27

• Level
Note the "X" in the energy_shift field for the 2nd level, and the value in the "ripl_shift" column (see the API guide for the meaning of the shift)

# LEVEL
df=pd.read_csv(livechart+"fields=levels&nuclides=236np")
df

	z	n	symbol	idx	energy_shift	energy	unc_e	ripl_shift	jp	jp_order	half_life	operator_hl	unc_hl	unit_hl	half_life_sec	unc_hl.1	decay_1	decay_1_%	unc_1	decay_2	decay_2_%	unc_2	decay_3	decay_3_%	unc_3	isospin	magnetic_dipole	unc_mn	electric_quadrupole	unc_eq	Extraction_date
0	93	143	Np	0		0			(6-)	1	153E+3		5	Y	4828209674112.576	157784629872.96	EC	86.3	0.8	B-	13.5	0.8	A	0.16	0.04						2021-08-27
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4	93	143	Np	4		324	50		(5-)	1																					2021-08-27

5 rows × 31 columns

•Gamma

# GAMMA transitions
df=pd.read_csv(livechart+"fields=gammas&nuclides=135xe")
df

	z	n	symbol	start_level_idx	start_level_energy	unc_sle	start_level_jp	end_level_idx	end_level energy	unc_ele	end_level_jp	gamma_idx	energy	unc_en	relative_intensity	unc_ri	multipolarity	mixing_ratio	unc_mr	b_e1	unc_be1	b_e2	unc_be2	b_m1	unc_bm1	b_m2	unc_bm2	Extraction_date
0	54	81	Xe	1	288.455	0.015	1/2+	0	0.0		3/2+	0	288.451	0.016	100.0		[M1 E2]											2021-08-27
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
96	54	81	Xe	37	3169.900	0.900		26	2356.5	0.8		1	813.400	0.9	75.0	20												2021-08-27

97 rows × 28 columns

•Cumulative fission

# CUMULATIVE_FISSION
df=pd.read_csv(livechart+"fields=cumulative_fy&products=135xe")
df

	z_daughter	a_daughter	element_daughter	z_parent	z_parent.1	element_parent	metastable	cumulative_thermal_fy	unc_ct	cumulative_fast_fy	unc_cf	cumulative_14mev_fy	unc_c14	Extraction_date
0	54	135	Xe	92	235	U	0	0.06614	0.002249	0.06321	0.001833	0.064242	0.018117	2021-08-27
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
15	54	135	Xe	94	241	Pu	1	0.012532	0.0013048	0.01377	0.001443			2021-08-27

16 rows × 14 columns

•Independent fission

# INDEPENDENT FISSION
df=pd.read_csv(livechart+"fields=independent_fy&parents=235u")
df

	z_daughter	a_daughter	element_daughter	z_parent	a_parent	element_parent	metastable	independent_thermal_fy	unc_it	independent_fast_fy	unc_if	independent_14mev_fy	unc_i14	Extraction_date
0	1	1	H	92	235	U	0	0.00001711	0.0000029483	0.000026891	0.0000091812	0.000026369	0.0000090796	2021-08-27
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1230	72	179	Hf	92	235	U	0					0	0	2021-08-27

1231 rows × 14 columns

•Decay radiation

# DECAY_RADIATION, type alpha
df=pd.read_csv(livechart+"fields=decay_radiations&nuclides=238u&rad_types=a")
df

	3

# DECAY_RADIATION, type gamma
df=pd.read_csv(livechart+"fields=decay_rads&nuclides=238u&rad_types=g")
df

	energy	unc_en	intensity	unc_i	start_level_energy	end_level_energy	multipolarity	mixing_ratio	unc_mr	conversion_coeff	unc_cc	p_z	p_n	p_symbol	p_energy_shift	p_energy	unc_pe	jp	half_life	operator_hl	unc_hl	unit_hl	half_life_sec	unc_hls	decay	decay_%	unc_d	q	unc_q	d_z	d_n	d_symbol	Extraction_date
0	44.915				0.0	E2		NaN			92	146	U		2557.9	0.5	0+	2.800000e+02		6	ns	2.800000e-07	6.000000e-09	IT	97.4	0.4		NaN	92	146	U	2021-08-27	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
13	108.580		0.000134								92	146	U		0.0		0+	4.468000e+09		6	Y	1.409963e+17	1.893416e+14	A	100.0		4269.9	2.1	90	144	Th	2021-08-27	NaN

14 rows × 33 columns

# DECAY_RADIATION, type x-ray
df=pd.read_csv(livechart+"fields=decay_rads&nuclides=238u&rad_types=x")
df

	energy	unc_en	intensity	unc_i	type	shell	p_z	p_n	p_symbol	p_energy_shift	p_energy	unc_pe	jp	half_life	operator_hl	unc_hl	unit_hl	half_life_sec	unc_hls	decay	decay_%	unc_d	q	unc_q	d_z	d_n	d_symbol	Extraction_date
0	15.784		7.300000		X	L	92	146	U		0		0+	4.468000e+09		6	Y	1.409963e+17	189341555847552	A	100		4269.9	2.1	90	144	Th	2021-08-27
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
5	108.580		0.000134		X	KpB2	92	146	U		0		0+	4.468000e+09		6	Y	1.409963e+17	189341555847552	A	100		4269.9	2.1	90	144	Th	2021-08-27

6 rows × 28 columns

# DECAY_RADIATION, type beta-
df=pd.read_csv(livechart+"fields=decay_rads&nuclides=135xe&rad_types=bm")
df

	mean_energy	unc_me	intensity_beta	unc_ib	daughter_level_energy	max_energy	unc_me.1	log_ft	unc_lf	transition_type	anti_nu_mean_energy	unc_ame	p_z	p_n	p_symbol	p_energy_shift	p_energy	unc_pe	jp	half_life	operator_hl	unc_hl	unit_hl	half_life_sec	unc_hls	decay	decay_%	unc_d	q	unc_q	d_z	d_n	d_symbol	Extraction_date
0	26.9	1.1	0.123	0.006	1062.42	103	4	5.71	0.06		76.0	2.9	54	81	Xe		0.000		3/2+	9.14		2	h	32904.0	72	B-	100.0		1169	4	55	80	Cs	2021-08-27
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9	641.4	1.7	0.600	0.600	0.00	1692	4	8.50		1U	605.2	3.5	54	81	Xe		526.551	0.013	11/2-	15.29		5	m	917.4	3	B-	0.6		1169	4	55	80	Cs	2021-08-27

10 rows × 34 columns

# DECAY_RADIATION, type electron
df=pd.read_csv(livechart+"fields=decay_rads&nuclides=238u&rad_types=e")
df

	energy	unc_en	intensity	unc_i	type	shell	p_z	p_n	p_symbol	p_energy_shift	p_energy	unc_pe	jp	half_life	operator_hl	unc_hl	unit_hl	half_life_sec	unc_hls	decay	decay_%	unc_d	q	unc_q	d_z	d_n	d_symbol	Extraction_date
0	13.113		8.100000		AU	L	92	146	U		0		0+	4.468000e+09		6	Y	1.409963e+17	189341555847552	A	100		4269.9	2.1	90	144	Th	2021-08-27
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4	104.327		0.000004		AU	KXY	92	146	U		0		0+	4.468000e+09		6	Y	1.409963e+17	189341555847552	A	100		4269.9	2.1	90	144	Th	2021-08-27

5 rows × 28 columns

# DECAY_RADIATION, type beta+/electron capture
df=pd.read_csv(livechart+"fields=decay_rads&nuclides=236np&rad_types=bp")
df

	mean_energy	unc_me	intensity_beta	unc_ib	daughter_level_energy	energy_ec	unc_eec	intensity_ec	unc_ie	log_ft	unc_lf	transition_type	nu_mean_energy	unc_nme	p_z	p_n	p_symbol	p_energy_shift	p_energy	unc_pe	jp	half_life	operator_hl	unc_hl	unit_hl	half_life_sec	unc_hls	decay	decay_%	unc_d	q	unc_q	d_z	d_n	d_symbol	Extraction_date
0	NaN				149.46	781	50	6.04	0.06	15.7		1U			93	143	Np		0		(6-)	153000.0		5	Y	4.828210e+12	1.577846e+11	EC	86.3	0.8	934	50	92	144	U	2021-08-27
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
5	NaN				309.79	620	50	74.50	1.00	14.1		1NU			93	143	Np		0		(6-)	153000.0		5	Y	4.828210e+12	1.577846e+11	EC	86.3	0.8	934	50	92	144	U	2021-08-27

6 rows × 36 columns